Safak Dogan

The paper proposes an enhanced Wasserstein GAN with Gradient Penalty (SA-JS-WGAN-GP) incorporating Self-Attention and Jensen-Shannon Divergence to synthesize diverse network traffic data, significantly improving the detection of zero-day attacks in Intrusion Detection Systems (IDS).

The paper introduces GMA-SAWGAN-GP, a novel generative framework that significantly enhances Intrusion Detection System (IDS) performance by augmenting mixed-type network traffic data, especially improving generalization to unknown attacks.

The paper introduces a novel byte-level method to encode network flow records into fixed-size RGB images, significantly improving the performance of Intrusion Detection Systems (IDS) by allowing convolutional architectures to exploit spatial correlations.